code: def forward_gpu(self, inputs):
      self.retain_inputs((0,))
      x = inputs[0].reshape(len(inputs[0]), -1)
      l2normsquared_kernel = cuda.reduce(
          "T x", "T y", "x * x", "a + b", "y = a", "0", "l2normsquared"
      )
      return (l2normsquared_kernel(x, axis=1),)
  
file_path: chainer/chainer/functions/math/batch_l2_norm_squared.py
imports: import six
  import chainer
  from chainer.backends import cuda
  from chainer import function_node
  from chainer.functions.math import sum
  from chainer.utils import type_check
label_desc: Use image node_id 3 for calling the BatchL2NormSquared obj's underlying member method code with example usage: obj.forward_gpu(inputs) and returns: l2normsquared_kernel